Add README.md with model card

README.md

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+---
+library_name: scikit-learn
+tags:
+- classification
+- tabular-data
+metrics:
+  accuracy: 0.6629
+  precision: 0.6890
+  recall: 0.8482
+  f1: 0.7603
+params: {"max_depth": 10, "min_samples_leaf": 1, "min_samples_split": 10, "n_estimators": 200}
+---
+# Random Forest Classifier for Engine Condition Prediction
+
+This repository contains a trained `RandomForestClassifier` model for predicting engine condition (Normal vs. Faulty) based on various engine parameters.
+
+## Model Details
+
+-   **Algorithm**: RandomForestClassifier
+-   **Framework**: scikit-learn
+
+## Performance Metrics (on Test Set)
+
+-   **Accuracy**: 0.6629
+-   **Precision**: 0.6890
+-   **Recall**: 0.8482
+-   **F1-Score**: 0.7603
+
+## Hyperparameters
+
+```json
+{
+  "max_depth": 10,
+  "min_samples_leaf": 1,
+  "min_samples_split": 10,
+  "n_estimators": 200
+}
+```
+
+## Usage
+
+To load and use this model:
+
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(repo_id="HumanMachine74/engine-performance-data-model", filename="random_forest_model.joblib")
+model = joblib.load(model_path)
+
+# Example prediction (assuming X_new is your new data)
+# predictions = model.predict(X_new)
+```